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14.11.14

SfN 2014

This will be my first year attending SfN as an actual professor (I was hired but hadn't started by SfN 2013).

This means I'm on the lookout for potential PhD students and post-docs. Nothing certain yet, as grants haven't come back, but if you're looking for a place to do you PhD, or thinking about a post-doc in the next year or two, hit me up.

It turns out, San Diego's a pretty nice city, and has pretty good cognitive science and neuroscience programs (FALSE HUMILITY PRECEDING).

At the UCSD booth at the 4th Enhancing Neuroscience Diversity through Undergraduate Research Education Experiences (ENDURE) meeting on Saturday, Nov 15 from 9:30-11:00am at the Marriott Marquis, Independence Ballroom EFGH

At our book signing at the Princeton University Press booth from 11:00-12:00 on Monday, Nov 17

Neuronal oscillations play an important role in neural communication and network coordination. Low frequency oscillations are comodulated with local neuronal firing rates and correlate with a physiological, perceptual, and cognitive processes. Changes in the population firing rate are reflected by a broadband shift in the power spectral density of the local field potential. On top of this broadband, 1/f^α field, there may exist concurrent, low frequency oscillations. The spectral peak and bandwidth of low frequency oscillations differ among people, brain regions, and cognitive states. Despite this widely-acknowledged variability, the vast majority of research uses a priori bands of interest (e.g., 1-4 Hz delta, 4-8 Hz theta, 8-12 Hz alpha, 12-30 Hz beta). Here we present a novel method for identifying the oscillatory components of the physiological power spectrum on an individual basis, which captures 95-99% of the variance in the power spectral density of the signal with a minimal number of parameters. This algorithm isolates the center frequency and bandwidth of each oscillation, providing a blind method for identifying individual spectral differences. We demonstrate how automated identification of individual oscillatory components can improve neurobehavioral correlations and identify population differences in spectral and oscillatory parameters.

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Who I Am

Neuroscientist combining large scale data-mining, machine-learning techniques, and brain computer interfacing with hypothesis-driven experimental research to understand the relationships between the human frontal lobes, cognition, and disease. Into really geeky stuff. World zombie neuroscience expert. Also run brainSCANr.com with my wife, Jessica.